This paper presents a new modeling method of the continuous density Hidden Markov Model. As we know, speech signal is characterized by a hidden state sequence and each state is described by the mixture of weighted Gaussian density functions. Usually if we want to describe speech signal more precisely, we need to use more Gaussian functions for each state. But it will increase the computation si...

w e ppose a new algorithm for enhancing noisy speech which have been degraded by statistically independent additive noise. The al p rithm is based upon modeling the clean speech as a hidden Markov process with mixtures of Gaussian autoregressive (AR) output processes, and the noise process as a sequence of stationary, statistically independent, Gaussian AR vectors. The parameter sets of the mod...

We present a radio browsing system developed on a very small corpus of annotated speech by using semi-supervised training of multilingual DNN/HMM acoustic models. This system is intended to support relief and developmental programmes by the United Nations (UN) in parts of Africa where the spoken languages are extremely under resourced. We assume the availability of 12 minutes of annotated speec...

In this paper, we propose a novel boosted mixture learning (BML) framework for Gaussian mixture HMMs in speech recognition. BML is an incremental method to learn mixture models for classification problem. In each step of BML, one new mixture component is calculated according to functional gradient of an objective function to ensure that it is added along the direction to maximize the objective ...

Movie is a kind of complex video with rich content. The analysis of movie is more complicated than other types of videos like surveillance, sport games, and documentaries. In this paper, a statistical approach using hidden Markov model to classify movie scenes is proposed. Two important kinds of movie scenes, dialogue and fighting scenes, are classified. Color and motion features are extracted ...

This paper proposes a new type of acoustic model called the discrete mixture HMM (DMHMM). As large scale speech databases have been constructed for speaker-independent HMMs, continuous mixture HMMs (CMHMMs) are needed to increase the number of mixture components in order to represent complex distributions. This leads to a high computational cost for calculating output probabilities. The DMHMM r...

background: routinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. we aimed at using a hidden markov model in order to detect the abnormal states of pulmonary tuberculosis in iran. methods: data for this study were the weekly number of newly diagnosed cases with sputum smear-positive p...

Building on the current understanding of neural architecture of the visual cortex, we present a graphical model for learning and classification of motion patterns in videos. The model is composed of an arbitrary amount of Hidden Markov Models (HMMs) with shared Gaussian mixture models. The novel extension of our model is the use of additional Markov chain, serving as a switch for indicating the...

This paper presents the development of a Filipino speech recognition using the HTK System tools. The system was trained from a subset of the Filipino Speech Corpus developed by the DSP Laboratory of the University of the Philippines-Diliman. The speech corpus was both used in training and testing the system by estimating the parameters for phonetic hmm-based (Hidden-Markov Model) acoustic model...

This paper describes our attempt to make the Hidden Markov Model (HMM) score following system developed at Ircam sensible to past experiences in order to obtain better audio to score real-time alignment for musical applications. A new observation modeling based on Gaussian Mixture Models is developed which is trainable using a learning algorithm we would call automatic discriminative training. ...